estimate_mdiff_2x2_mixed {esci}

Estimates for a 2x2 mixed factorial design with a continuous outcome variable

Description

Returns object estimate_mdiff_2x2_mixed is suitable for a 2x2 mixed-factorial design with a continuous outcome variable. It estimates each main effect, the simple effects for the repeated-measures factor, and the interaction. It can express these estimates as mean differences. This function accepts raw data only. Standardized mean differences are not (yet) available; stay tuned. Median differences are also not yet available.

Usage

estimate_mdiff_2x2_mixed(
  data,
  outcome_variable_level1,
  outcome_variable_level2,
  grouping_variable,
  outcome_variable_name = "My outcome variable",
  repeated_measures_name = "Time",
  conf_level = 0.95,
  save_raw_data = TRUE
)

Arguments

data

For raw data - a dataframe or tibble

outcome_variable_level1

The column name of the outcome variable for level 1 of the repeated-measures factor

outcome_variable_level2

The column name of the outcome variable for level 2 of the repeated-measures factor

grouping_variable

The column name of the grouping variable; only 2 levels allowed; must be a factor

outcome_variable_name

Optional friendly name for the outcome variable. Defaults to 'My outcome variable' or the outcome variable column name if a data frame is passed.

repeated_measures_name

Optional friendly name for the repeated measures factor. Defaults to 'Time'

conf_level

The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95.

save_raw_data

For raw data; defaults to TRUE; set to FALSE to save memory by not returning raw data in estimate object

Details

Reach for this function in place of a 2x2 mixed-factorial ANOVA.

Once you generate an estimate with this function, you can visualize it with plot_mdiff() and you can visualize the interaction specifically with plot_interaction(). You can test hypotheses with test_mdiff().

The estimated mean differences are from statpsych::ci.2x2.mean.mixed().

Value

Returns object of class esci_estimate

• es_mean_difference

  • type -

  • outcome_variable_name -

  • grouping_variable_name -

  • effect -

  • effect_size -

  • LL -

  • UL -

  • SE -

  • df -

  • ta_LL -

  • ta_UL -

  • effect_type -

  • effects_complex -

  • t -

  • p -

• es_smd

  • outcome_variable_name -

  • grouping_variable_name -

  • effect -

  • effect_size -

  • LL -

  • UL -

  • numerator -

  • denominator -

  • SE -

  • df -

  • d_biased -

  • effect_type -

  • effects_complex -

• overview

  • outcome_variable_name -

  • grouping_variable_name -

  • grouping_variable_level -

  • mean -

  • mean_LL -

  • mean_UL -

  • median -

  • median_LL -

  • median_UL -

  • sd -

  • min -

  • max -

  • q1 -

  • q3 -

  • n -

  • missing -

  • df -

  • mean_SE -

  • median_SE -

• raw_data

  • grouping_variable -

  • outcome_variable -

  • grouping_variable_A -

  • grouping_variable_B -

  • paired -

Examples

# From raw data (summary data mode not available for this function)
example_data <- data.frame(
  pretest = c(
    19, 18, 19, 20, 17, 16, 16, 10, 12,  9, 13, 15
  ),
  posttest = c(
    18, 19, 20, 17, 20, 16, 19, 16, 16, 14, 16, 18
  ),
  condition = as.factor(
    c(
      rep("Control", times = 6),
      rep("Treated", times = 6)
    )
  )
)

estimates <- esci::estimate_mdiff_2x2_mixed(
  data = example_data,
  outcome_variable_level1 = pretest,
  outcome_variable_level2 = posttest,
  grouping_variable = condition,
  repeated_measures_name = "Time"
)
# To visualize the estimated mean difference for the interaction
myplot <- esci::plot_mdiff(estimates$interaction, effect_size = "mean")

# Line-plot of the interaction with fan effect representing each simple-effect CI
plot_interaction_line_CI <- esci::plot_interaction(
  estimates,
  show_CI = TRUE
)

# To conduct a hypothesis test
res_htest_from_raw <- esci::test_mdiff(
  estimates$interaction,
  effect_size = "mean"
 )